The relationship between the use of screen-based devices and self-reported sleep quality in adolescents aged 13–19 years in Brunei

Abstract

Background

The widespread use of digital devices among adolescents has raised concerns about the potential impact of screen time on sleep quality. This study aimed to investigate the relationship between screen time and self-reported sleep quality in adolescents.

Methods

A cross-sectional study was conducted on adolescents (13- to 19-year-olds) using multi-stage cluster sampling with probability proportional to the size of public schools. Data were collected in November 2022 through self-administered questionnaires. The questionnaire collected sociodemographic characteristics, sleep quality using the Pittsburgh Sleep Quality Index (PSQI), and screen time on weekdays and weekends using the Screen Time Questionnaire (STQ). A scoring system was used in the PSQI and a global score of more than 5 indicates poor sleep quality. The relationship between screen time and sleep quality was analysed using simple and multiple linear regression.

Results

A total of 547 adolescents participated in the study, with a mean (SD) age of 16.66 (1.54) years. The mean (SD) PSQI score was 5.98 (2.70), and 52% of participants had poor sleep quality. The sleep disturbance component had the highest mean (SD) score at 1.35 (0.5) out of a total score of 3.0. The mean (SD) screen time for a weekday was 537.6 (301.5) minutes, and a weekend day was 725.5 (339.2) minutes. The highest median screen time was spent on smartphones during the whole week. A significant linear relationship was observed between age and PSQI global score (p = 0.008), with a 0.2 increase in PSQI global score for each year increase in age (95% CI: 0.05, 0.35). Being female was also significantly associated with a high PSQI global score (p < 0.001). Additionally, a significant linear relationship was observed between screen time on a weekend and PSQI score (p = 0.032).

Conclusions

The study found that half of the adolescents had poor sleep quality, which was associated with being female, increased screen time on weekends, and older age. Future research endeavours should focus on conducting longitudinal studies to assess the temporal relationship between screen time and sleep quality.

Background

Many high school students report tiredness every morning due to deficient sleep at night [1]. Centre for Disease Control and Prevention recommends that an average adolescent get 8–10 h of sleep per night, but very few adolescents comply with it [2]. Sleep deprivation has become an increasingly significant public health issue in recent years; a study conducted in America reported that 72.7% of high school students get less than the recommended amount of sleep [3]. Poor sleep quality was also reported in 72.5% of the adolescents who participated in a cross-sectional study conducted in Malaysia, making it apparent that sleep issues are common in this part of the world too [4].

Adequate sleep is vital for good health and greatly impacts attention, cognition, and mood, all of which are of extreme importance to a growing adolescent [5]. According to a review by Itani et al. (2017) [6], short sleep duration has been linked to diseases such as diabetes mellitus, hypertension, cardiovascular diseases, coronary heart disease, obesity and a significant increase in mortality rate in the long term. Sleep disorders in adolescents have also been associated with depressive symptoms and poor academic performance [7, 8]. In 2015, to highlight the importance of sleep, the University of Brunei Darussalam conducted a three-day sleep awareness campaign. This initiative included several activities, such as engaging in educational games and screening for daytime sleepiness. It was open to all university staff and students, as they were believed to be more at risk due to both academic and social pressures. In addition to this, a recent study has explored the sleep quality of healthcare workers such as nurses in Brunei, however, no literature assesses the prevalence of sleep issues and its contributing factors amongst adolescents locally [9].

The quality of sleep of adolescents is affected by numerous aspects of their lives. Adolescence is a crucial period of growth where individuals go through complicated stages of puberty and experience major hormonal changes. There is a biological shift in their sleep-wake cycle with a disparity between their desired bedtime and the requirement to be up early due to social necessities such as attending school [10]. In addition to this, mental health issues such as clinical depression, negative perception of their health and social anxiety have all been associated with poor sleep outcomes in adolescents [11]. Other contextual factors such as violence in the community, racial discrimination, and poverty in the neighbourhood are issues that play a role in poor sleep quality [12]. The list of contributing factors for sleep insufficiency continues to grow, but one that stands out is screen time, more so in recent years than in the past few decades [13, 14]. The use of digital screens in today’s world of technological advancement has become more of a necessity than a luxury with its use being integrated into education and daily lives a lot more intricately than ever before.

In the last decade, the use of portable devices such as laptops, tablets and mobile phones has shown great potential as learning tools both within the classroom and outdoors [15]. It has specifically become an essential part of education during the COVID-19 pandemic, where many countries resorted to online schooling to curb the spread of the virus, significantly increasing the daily duration of screen exposure [16]. A meta-analysis of 46 studies reported a 52% increase in the use of screens during the COVID-19 pandemic in children and adolescents [17] Increased screen time was also observed in a study conducted in Brunei during the pandemic on students and lecturers in healthcare education [18]. The use of social media is reported to have many mental health benefits for adolescents by allowing them to remain connected to their peers, and reduce the prevalence of depressive symptoms [19]. Moreover, a review of 17 articles revealed that active video games may promote physical fitness and improve their self-esteem [20].

Although many students use laptops and phones for educational and other positive purposes, a lot of negative impacts of this exposure have been identified. Around 62% of adolescents spend more than four hours a day on screen media, and 29% use screens more than eight hours a day in a study conducted in America, which is astounding [21]. In Southeast Asia, the prevalence of internet addiction was 20%, and gaming disorders were 10.1%, which is higher than what is generally observed around the rest of the world [22]. The use of screens, especially mobile phones, has a negative impact on the total duration and quality of sleep adolescents get at night. They have more frequent mobile-related awakenings at night and suffer from sleep latency and restless sleep [23]. In a review of studies associated with sleep and screen time, 90% of the 67 studies reviewed showed an adverse association of screen time with sleep, where the duration of sleep was shortened and bedtime delayed [24].

Due to the increase in screen time and consequent reduction in sleep duration, behavioural problems have been reported in school-aged children [25]. A qualitative study conducted in Brunei on the use of social media by adolescents revealed that to continue chatting online with their friends, they stayed up late and delayed bedtime, thereby shortening the duration of their nightly sleep [26]. Furthermore, a study conducted on children under four in Brunei disclosed that most of the children exceeded the less than 1 h of screen time per day recommended for their age, and of this, 50% were on interactive content while only 21.6% were on educational content [27]. In adolescents, decreased exposure to screen-related electronic devices in the evenings has been associated with better nighttime sleep and daytime alertness [28].

Given the many benefits as well as numerous drawbacks associated with screen utilisation, in the current world of digitalisation, it is imperative to thoroughly explore the literature regarding sleep quality and excessive screen time in Brunei. The majority of the studies conducted locally have focused on specific aspects of internet use and video gaming, but no study has been carried out to explore the relationship between the use of screen-based devices and sleep quality in adolescents. Given the gaps in the literature, we aimed to describe sleep quality, and duration of screen time for all primary types of devices that adolescents use (e.g., smartphones, tablets, video games, laptops), and to explore the relationship between the screen time and the sleep quality among adolescents aged 13 to 19 years. We hypothesised that more usage of screen-based devices was associated with poorer sleep quality among these adolescents.

Methods

Study design, study population and sample

A cross-sectional study was conducted in November 2022. The target population were adolescents aged 13 to 19 years residing in Brunei Darussalam recruited from secondary and sixth-form public schools. Students between Year 8 to 10 and Year 12 from these schools were included but Year 11 students were not included in this study as they were occupied with exams during data collection. Those who were less than 13 years or older than 19 years, at the time of the data collection, were excluded from the study. This age range was a commonly used definition of adolescents identified in a systematic review assessing screen time and sleep in adolescents, thereby making it easier to make comparisons [24].

The sample size was determined using SCALEX calculator [29]. We required a sample size of 127 for the precision of ± 1 h in estimating the mean duration of screen time with a 95% confidence interval (CI). The standard deviation (SD) of screen time was estimated as 5.74 h [30]. Considering possible missing data and non-response of 15%, we estimated to take 150 students. Considering a multi-stage cluster sampling and the commonly used design effect of two [31], we required a minimum of 300 students.

The multistage cluster sampling applied in this study was as follows: One school from each of the six existing clusters of secondary public schools was selected using simple random sampling with probability proportional to size, and all five public sixth-form schools were included in the study without sampling. In the second stage, 150 students each from the chosen schools studying in Years 8, 9, 10 and 12 were selected using simple random sampling.

Data collection

Data was collected using validated questionnaires to assess the students’ screen time usage and sleep conditions. Data collection took place during school hours. Envelopes containing a set of questionnaires, parent consent forms, participant consent forms, and a participant information sheet together with instructions were handed over by the research team to the class teachers of the selected schools. The class teachers distributed the questionnaires, consent forms and participant information sheet to each student to be filled out at their convenience and returned the following day to the class teacher. Before the distribution, students were briefed by the class teacher, regarding how anonymity is maintained in the study and their rights to withdraw from the study. The class teachers then put completed questionnaires and consent forms into sealed envelopes which were later collected by the researchers.

Research instruments

The questionnaires included three components: Sociodemographic characteristics, the Pittsburgh Sleep Quality Index (PSQI), and screen time.

Outcome variable: sleep quality

The PSQI, a validated, self-reporting questionnaire was used to assess the sleep quality in the past month [32]. It included seven domains with a total of 19 items. A total score of more than 5 indicates poor sleep quality. The original questionnaires reported a good validity having a diagnostic sensitivity of 89.6% and specificity of 86.5%, and a good reliability with kappa = 0.75 [32].

The Malay version of the PSQI was validated by Farah et al. (2019), having internal consistency reliability (Cronbach alpha) of 0.74 and test-retest reliability (intra-class correlation coefficient, ICC) of 0.58 [33]. Cronbach’s alpha is a statistic used to measure the extent to which different items measure the same construct, and a statistic of ≥ 0.7 is considered reasonable evidence of reliability [34]. The intraclass correlation coefficient is considered reasonable evidence of reliability at two time points and values of ≥ 0.7 are considered reasonable evidence of reliability [35].

Exposure variable: screen time

The screen time questionnaire (STQ) comprises four domains and a total of 18 items including questions on screen use on an average weekday, weeknight, weekend day and background screen [36]. It includes the use of electronic devices with screens, regularly used by adolescents, such as smartphones and tablets. All the items in the questionnaire, apart from the question asked regarding the use of smartphones on weekends, have fair to excellent reliability (Intraclass correlation of 0.50 to 0.90) [36]. For this study, the research team translated the original English version of the questionnaire into Malay. Additionally, a back-translation was done to verify the accuracy of the translation.

To ensure linguistic inclusivity, we incorporated all the questionnaires in both English and Malay languages for each item. We then pretested the bilingual questionnaires among 11 apprentices employed in the Ministry of Education, ensuring that the questions were easily understood. Additional explanations were added based on the feedback from the pretests. We noted that the average time required to complete the questionnaire [37] was about 30 min.

Covariates

Between screen time and sleep quality, we controlled for some personal variables which could have an impact on the sleep quality such as age, gender, ethnicity, grade or year in school, and parents’ education. These variables are included in the first part of the questionnaire (sociodemographic characteristics).

Data analysis

The data was entered into Microsoft Excel and cleaned and analysed by using R version 4.1.1 and RStudio version 1.4.1717 for Windows. Descriptive statistics such as counts and percentages for categorical variables, mean and standard deviation (SD) for normally distributed numerical variables, and median and interquartile range (IQR) for skewed numerical variables. The relationship between screen time as an exposure variable and sleep quality as an outcome variable was analysed using simple and multiple linear regression (SLR and MLR respectively). In this analysis, we tested the potential confounders (covariates) such as age, gender, ethnicity, grade or year in school, and parents’ education. We started with simple linear regression for each independent variable (exposure and covariate) with the outcome (sleep quality index). Then, we subjected all independent variables to a stepwise automatic variable selection procedure using the MASS R package. We then tested the selected and omitted variables manually one-by-one using model comparison, F test. In the next step, the model with selected variables is checked for multicollinearity by obtaining variance-inflation-factor (vif) using the CAR R package, and for interactions among the variables. After that, we checked model fitness or linearity, equal variance and normal distribution of residuals by obtaining residual plots. After all tests were successful, we presented the results in a table. p values less than 0.05 were considered statistically significant.

Results

Characteristics of respondents

A total of 635 students agreed to participate in the study. However, 88 of them did not complete or returned the questionnaires without parental consent. Characteristics of 547 respondents are presented in Table 1. The mean (SD) age was 16.66 (1.54) years. The majority of the respondents were female (62.3%), Malay (81.8%), of Year 12 (43.5%), and reported their parental education level as secondary schools (40.0% and 38.8% for father and mother respectively).

Table 1.

Participants’ characteristics (n = 547)

Variable	n (%)
Age (Year)	16.66 (1.54)a
Gender
Male	206 (37.7)
Female	341 (62.3)
Ethnicity
Malay	444 (81.8)
Chinese	42 (7.7)
Others	61 (11.2)
Year/Grade
Year 8	85 (15.5)
Year 9	82 (15.0)
Year 10	142 (26.0)
Year 12	238 (43.5)
Father’s Education
Primary school or lower	23 (4.3)
Secondary school	214 (40.0)
Sixth form	29 (5.4)
HND or higher	118 (22.0)
Not sure	152 (28.4)
Mother’s Education
Primary school or lower	14 (2.6)
Secondary school	208 (38.8)
Sixth form	44 (8.2)
HND or higher	137 (25.6)
Not sure	133 (24.8)

^a Mean (Standard Deviation);

HND = Higher National diploma

Participants’ sleep quality

The details of PSQI components and the respective scores are presented in Table 2. The highest mean (SD) score was for sleep disturbance which was 1.35 (0.58) out of a total score of 3, followed by subjective sleep quality, daytime dysfunction and sleep latency, which had similar scores such as 1.07 (0.64), 1.03 (0.65), and 1.02 (0.81), respectively. Sleep duration had the highest proportion of students (9.0%) at the highest score of 3. The mean (SD) of the PSQI global score was 5.98 (2.70). Overall, half of respondents (52%) had poor sleep quality with a global score of more than 5.

Table 2.

Pittsburgh sleep quality index and global scores (n = 547)

Component (C)	Score (0 to 3)				Mean (SD)
	Score (0) n (%)	Score (1) n (%)	Score (2) n (%)	Score (3) n (%)
C1: Subjective sleep quality	80 (14.6)	362 (66.2)	91 (16.6)	14 (2.6)	1.07 (0.64)
C2: Sleep latency	150 (27.4)	259 (47.3)	115 (21.0)	23 (4.2)	1.02 (0.81)
C3: Sleep duration	210 (38.4)	193 (35.3)	95 (17.4)	49 (9.0)	0.97 (0.96)
C4: Habitual sleep efficiency	420 (76.8)	57 (10.4)	32 (5.9)	38 (6.9)	0.43 (0.88)
C5: Sleep disturbance	18 (3.3)	331 (60.5)	186 (34.0)	12 (2.2)	1.35 (0.58)
C6: Sleep medication	508 (93.0)	25 (4.6)	10 (1.8)	3 (0.5)	0.10 (0.40)
C7: Daytime dysfunction	97 (17.7)	346 (63.3)	94 (17.2)	10 (1.8)	1.03 (0.65)
Global scorea (0–21)					5.98 (2.70)

SD: Standard Deviation; ^a higher score indicates worse sleep problems;

A comparison of PSQI components and global scores between male and female adolescents is presented in Table 3. The mean (SD) score was significantly different in 4 components; subjective sleep quality, sleep latency, sleep disturbance and daytime dysfunction (p < 0.05). The mean (SD) of the global score of males and females were 5.35 (2.51) and 6.34 (2.74) respectively, which were also significantly different (p < 0.001).

Table 3.

Pittsburgh sleep quality index^b and global score^b for males (n = 206) and females (n = 341)

	Male Mean (SD)	Female Mean (SD)	t stat.	df	p-value
C1: Subjective sleep quality	0.92 (0.57)	1.16 (0.66)	-4.44	545	< 0.001
C2: Sleep latency	0.87 (0.77)	1.11 (0.82)	-3.32	545	< 0.001
C3: Sleep duration	0.89 (0.89)	1.02 (0.99)	-1.54	545	0.130
C4: Habitual sleep efficiency	0.35 (0.81)	0.48 (0.92)	-1.55	545	0.120
C5: Sleep disturbancea	1.24 (0.57)	1.42 (0.58)	-3.58	434	< 0.001
C6: Sleep medication	0.13 (0.49)	0.08 (0.33)	1.26	545	0.210
C7: Daytime dysfunctiona	0.96 (0.59)	1.08 (0.68)	-2.18	482	0.030
Global scoreb (0–21)	5.35 (2.51)	6.34 (2.74)	-4.22	545	< 0.001

SD: Standard Deviation; ^a Independent t-test (equal variance not assumed); ^b higher index/score indicates worse sleep problems;

Participants’ screen time

Descriptive statistics for screen time (in minutes) are presented in Table 4. The highest median (IQR) screen time was spent on smartphones during the whole week, with the median (IQR) screen time per day on a weekday being 300 (300) minutes, after school hours 240 (240) minutes, and at weekend day 400 (360) minutes. This was followed by television and laptop on a weekend day with a median (IQR) of 60 (138) minutes and 35 (180) minutes respectively. For a weekday the median (IQR) for laptops was 30 (120) minutes and television was 30 (90) minutes. The mean (SD) total screen time for weekdays (at school) was 537.6 (301.5) minutes or nearly 9 h, for weekdays after school was 460.9 (273.5) minutes or 7.7 h and for weekends was 725.5 (339.2) minutes or 12 h. Overall, the total screen time was higher on weekdays than at weekends.

Table 4.

Screen time on a weekday. After school and weekend day (n = 547)

Type of Screen		Screen time (in minutes/day)
		Median	IQR	Mean (SD)
Weekday (at school)
	TV	30	90
	Video games	0	60
	Laptop/Computer	30	120
	Smartphone	300	300
	Tablet	0	0
	Total	485.0	390.0	537.6 (301.5)
Weekday (after school)
	TV	0	60
	Video games	0	60
	Laptop/Computer	0	120
	Smartphone	240	240
	Tablet	0	0
	Total	420.0	330.0	460.9 (273.5)
Weekend
	TV	60	138
	Video games	0	102.5
	Laptop/Computer	35	180
	Smartphone	400	360
	Tablet	0	0
	Total	717.0	480.0	725.2 (339.2)

IQR: Interquartile range

Screen time and other factors associated with the global PSQI score (sleep quality)

Factors associated with the global PSQI score using SLR are presented in Table 5. Age, gender, Year/grade, screen time on a weekday and screen time on a weekend were significantly associated with the global PSQI score. All the variables, including all sociodemographic and screen time variables were then entered into an MLR model in which only gender emerged as a significant variable (Table 6). However, as this this model has non-significant variables in it, it could be an over-fit model. Therefore, the stepwise variable selection process was performed and three variables were independently associated with global PSQI score (Table 7). A significant positive linear relationship was observed between age and PSQI global score (p = 0.008). Those who were a year older were likely to have an increase of the PSQI global score by 0.200 (95% CI: 0.05, 0.35). Being female was significantly higher in global score by 0.992 units than being male (p < 0.001). A significant linear relationship was also observed between screen time on a weekend and PSQI score, where an hour’s increase in screen time was associated with a 0.044 unit increase in PSQI global score (p = 0.032).

Table 5.

Factors associated with sleep quality PSQI global score using simple linear regression (n = 547)

Independent variables	Simple linear regression
	Cru. b	(95% CI)	p-value
Age, years	0.231	(0.084, 0.377)	0.002
Gendera (Female)	0.989	(0.528, 1.449)	< 0.001
Ethnicityb
Chinese	-0.666	(-1.521, 0.189)	0.126
Others	-0.228	(-0.951, 0.496)	0.537
Year/Grade c
Year 9	0.445	(-0.367,1.257)	0.282
Year 10	0.878	(0.158, 1.597)	0.017
Year 12	1.176	(0.513, 1.839)	< 0.001
Father’s Education
Secondary school	-0.255	(-1.420, 0.910)	0.667
Sixth form	-0.121	(-1.604, 1.361)	0.872
HND or higher	0.091	(-1.119, 1.301)	0.883
Not sure	-0.159	(-1.347, 1.029)	0.792
Mother’s Education
Secondary school	-0.655	(-2.118, 0.809)	0.380
Sixth form	-0.224	(-1.851, 1.403)	0.787
HND or higher	-0.363	(-1.850, 1.125)	0.632
Not sure	-0.414	(-1.903, 1.076)	0.586
Weekday screen timee	0.059	0.014, 0.104)	0.010
Afterschool screen timee	0.049	(-0.000, 0.099)	0.052
Weekend screen timee	0.055	(0.015, 0.095)	0.007

Note: Sleep quality PSQI global score as the dependent variable

Reference level: ^a Male; ^b Malay; ^c Year 8; ^d Primary school or lower; ^e total screen time (hour)

CI = Confidence interval; Cru. b = crude regression coefficient;

Table 6.

Factors associated with sleep quality PSQI global score using multiple linear regression (n = 547)

Independent variable	Multiple linear regression
	Adj. b	(95% CI)	t-stat	p-value
Age, years	-0.16	(-0.61, 0.29)	-0.70	0.486
Genderb (Female)	0.97	(0.50, 1.44)	4.06	< 0.001
Weekend screen timec	0.03	(-0.02, 0.08)	1.16	0.248
Ethnicity	-0.04	(-0.39, 0.30)	-0.26	0.796
Year/Grade at school	0.37	(-0.08, 0.82)	1.61	0.108
Father’s education	-0.03	(-0.26, 0.20)	-0.25	0.803
Mother’s education	0.05	(-0.19, 0.29)	0.40	0.687
Weekday screen timee	0.02	(-0.04, 0.08)	0.67	0.505
After school screen timee	0.01	(-0.05, 0.06)	0.17	0.863

Note: Sleep quality PSQI global score as the dependent variable

^aR² was 0.053; No interaction; No multicollinearity problem; residuals were normally distributed

Reference level: ^b Male; ^c total screen time in hour

CI = Confidence interval; Adj. b = adjusted regression coefficient;

Table 7.

Significant factors associated with sleep quality PSQI global score using multiple linear regression (n = 547)

Significant independent variable	Multiple linear regression
	Adj. b	(95% CI)	t-stat	p-value
Age, years	0.200	(0.053,0.348)	2.678	0.008
Genderb (Female)	0.992	(0.529, 1.455)	4.208	< 0.001
Weekend screen timec	0.044	(0.004, 0.084)	2.153	0.032

Note: Sleep quality PSQI global score as the dependent variable

^aR² was 0.053; No interaction; No multicollinearity problem; Residuals were normally distributed

Reference level: ^b Male;

^c total screen time in hour

CI = Confidence interval; Adj. b = adjusted regression coefficient;

Discussion

This paper aimed to evaluate the association between the usage of screen-based devices on the self-reported sleep quality of adolescents. The mean age of the participants was 16.66 years, and most (62.3%) were female. The study found that more than half of the participants had poor sleep quality. These participants also showed a general increased screen time thus suggesting a negative impact of excessive screen use on sleep. Our study revealed that being female, older adolescents, and increased screen time during the weekends were independently associated with poor sleep outcomes in the participants. These findings are of substantial concern as they indicate that increased screen-based activities lead to sleep disruption in adolescents, which is known to have adverse effects on their health as well as physical and mental well-being.

In this study, 52% of the students had poor sleep quality, with sleep disturbance being the most challenging factor which is characterised by disorders that include difficulty initiating and maintaining sleep. These results were aligned with that of a much larger study conducted in a similar target population, on nearly 1500 adolescent students of grades 9, 10, 11 and 12 attending high schools in Turkey that disclosed poor sleep quality among 61.6% of its participants [38]. Another study conducted on adolescents aged 12 to 20 years in Turkey reported a much higher prevalence of 82% of the participants had poor sleep quality and 40% of them recognised and rated their quality of sleep as poor [39]. However, another cross-sectional study in Nepal reported a lower prevalence 31% of their 390 adolescent participants in grades 9 and 10 [40]. These findings are comparable across the studies as they all used the PSQI questionnaire as the tool to assess sleep, similar to this research. The dissimilarity in prevalences observed in sleep quality could be attributed to a number of factors that include geographical and socio-economic differences in the study participants among the studies. Consistent with the results from this paper, the sleep disturbance component had relatively high mean scores in other studies assessing adolescents’ sleep as well [38, 41]. Thus, it is apparent that sleep of inferior quality is highly prevalent among adolescents and young adults. High mean scores were observed in sleep latency, sleep disturbance and daytime dysfunction components in this study, all of which have been associated with social anxiety in young adults [11].

Over the past few years, the importance of sleep for adolescents has become more apparent, backed by a growing body of empirical evidence illustrating its extensive impact on their overall health. Specifically, sleep has been found to play a critical role in mitigating various health concerns among adolescents, including but not limited to obesity, mental health issues, illnesses, fatigue, accidents, and pain, such as headaches [42]. The National Sleep Foundation recommends for optimal health outcomes, adolescents should obtain between 8 and 10 h of sleep per day [43]. The present study’s participants indicate the sleep duration component’s comparatively low mean scores on the PSQI questionnaire. This finding suggests that, despite experiencing unsatisfactory sleep quality, participants still obtain sufficient hours of sleep. This underscores the importance of not only focusing on the quantity of sleep but also the quality of sleep as it can be considered a superior indicator of assessing sleep [44]. While several factors may contribute to diminished sleep quality among adolescents, excessive screen usage has emerged as a particularly salient issue in recent times. In fact, a systematic review conducted by Brautsch et al. (2023) [45] found a significant association between digital media use and compromised sleep outcomes, further underscoring the potential impact of screen time on sleep hygiene in this population.

The average screen time spent by the adolescents in this research on a weekday was nearly 9 h; on weekends, it increased up to 12 h. Notably, during school hours, participants indicated spending nearly 5 h on their smartphones. However, it is important to acknowledge that these estimates may be influenced by recall bias, as they rely on the participants’ memory and self-reporting. The increase in screen time at the weekends is consistent with existing literature. A cross-sectional survey conducted on parents and children revealed that recreational screen time at weekends was higher than on weekdays for all the members of the family [46]. A prospective cohort study of 5,048 children found that the proportion of extreme television users was 23% on school days and 30% on weekends [47]. Moreover, the values of screen time observed in this research are even higher than the daily 7.7 h of screen use observed in a large cross-sectional study conducted in the United States of America during the beginning of the COVID-19 pandemic on 5412 adolescents [30]. This highlights the need for interventions locally as high screen time increases the risk of sleep disturbance, potentially compromising the health and academic performances of adolescents, thereby reducing their overall quality of life.

During the pandemic, with limitations and protocols for social distancing in place, there was a sharp incline in the usage of digital devices for educational, social, and recreational activities [48]. During those trying times digital media use is reported to have had some beneficial effects by mitigating feelings of loneliness and stress in adolescents [49]. As this research was conducted just as the nation declared its endemicity, the high screen time usage recorded (12 h on a weekend) is possibly an after-effect. It could also be because some activities continued to be online and remained persistently high for more than a year after the relaxation of several public health measures [50].

Nonetheless, this sudden increase in screen use comes with its downside. A systematic review of reviews indicated that increased screen time negatively impacts the health of children and adolescents, particularly weight gain, unhealthy eating habits, feelings of depression, and overall quality of life [51]. This study significantly associated sleep of inferior quality with increased screen time on weekends. A systematic review of 23 articles evaluating the impact of the use of screens on the duration and quality of sleep amongst adolescents collectively found that the use of digital screens particularly at bedtime was related to inferior quality of sleep, insufficient duration of sleep and sleepiness during day [52]. A cross-sectional study conducted in India on 16,292 participants observed that the odds of having sleep problems were 1.55 times higher for adolescents aged 12 to 18 years (AOR: 1.55; CI: 1.21–1.99) and 1.48 times higher for young adults aged 19 to 23 years (AOR: 1.48; CI: 1.24–1.75) who spent more than 2 h on their smartphones in the last 24 h compared to those who did not [53]. Hence, strengthening the evidence of adverse repercussions on sleep resulting from the utilization of digital screens.

While there are many digital devices used by adolescents today, our study found that the highest median screen time was spent using smartphones compared with other screen-based devices, with adolescents spending up to 400 min on it on weekends. A study conducted in the United Arab Emirates (UAE) during the COVID-19 pandemic reported similar results, with adolescents spending seven hours per day (420 min) on average on smartphones [41]. Smartphone addiction which is a behavioural addiction that is measured by different scales and is generally difficult to define, was also reported in 39.7% of its 2,182 participants in a study conducted on medical students in China, and they attributed this to the modified lifestyle and behaviours brought on by the COVID-19 pandemic, where people started increasingly relying on the internet and smart devices [54, 55]. Smartphones have become a remarkably influential technological advancement due to their ability to perform a wide range of functions, portability, and growing prevalence [56]. Nevertheless, young people’s excessive use of smartphones and social media can lead to chronic sleep insufficiency and adversely impact their cognitive control, academic achievements, and socio-emotional well-being [57].

It was identified in this study that being female and older adolescents was associated with poor sleep quality. Interestingly, previous studies conducted on similar areas support this revelation. A study conducted in Turkey evaluating internet addiction revealed a significant relationship between this habit and negative sleep outcomes in females and as the grade of study of the participant increased, so did the rate of addiction to the internet [38]. The research by Bani-Issa et al. (2022) in the UAE also found independent associations between older adolescents and females with poor sleep outcomes [41]. A large-scale cross-sectional survey on 18, 642 children and adolescents in the United Kingdom during their first national lockdown during the COVID-19 outbreak also observed a higher deterioration of sleep in older adolescents and females than their male counterparts [58]. Moreover, the research conducted by Maurya et al. (2022) in India reported that the odds of having sleep problems were 2.11 times higher for females who used smartphones for less than or equal to 2 h (AOR: 2.11; CI: 1.63–2.73) and 2.94 times higher for females who used smartphones for 3 or more hours (AOR: 2.94; CI: 1.97–4.38) compared to adolescent males who did not use smartphone [53]. The present finding of greater susceptibility to suboptimal sleep quality among females is in alignment with established disparities in the incidence of insomnia within the adult population based on gender [59]. Consequently, greater emphasis ought to be placed on these adolescent individuals by engaging the assistance of their friends, parents, and educational institutions to enhance comprehension and adherence to sleep hygiene protocols.

Strengths and limitations

The current research represents a significant contribution to the literature. To the best of our knowledge, it is the first investigation conducted in Brunei Darussalam examining both sleep quality and digital screen use among adolescents. By addressing a gap in the existing knowledge base, this study adds value to the scientific discourse on sleep and screen time in the region. Although this research required a sample size of 300 students, data from over 500 students were analysed, deeming the findings of this research highly reliable and precise due to the larger sample size used in the analysis. Furthermore, the paper has been rigorously evaluated using a critical appraisal tool to assess its internal and external validity [60]. This assessment ensures that the study methods, data analysis, and results are robust and reliable. Therefore, this paper’s strength lies in its ability to provide valuable insights into the sleep habits and digital screen use of adolescents in Brunei Darussalam, while also adhering to quality scientific standards.

Nonetheless, the present study is subject to several limitations that may impact the interpretation and generalizability of the findings. Firstly, the data collection was limited to public schools, which may not be entirely representative of the target population in Brunei Darussalam. As such, caution should be exercised when applying the results of this study to the broader adolescent population. Secondly, using self-reported questionnaires to assess sleep quality and screen time is a potential limitation as it relies on the accuracy of participant recall. The accuracy of recall may be affected by a range of factors, including memory biases, social desirability bias, and individual interpretation of questions. Thirdly, the cross-sectional design of the study makes it impossible to establish a causal relationship between screen time and sleep quality. Further, longitudinal studies would be needed to investigate the potential causal mechanisms underlying the observed association. Fourthly the pretest was conducted on i-Ready officers who are graduates and do not match the age group assessed in this study due to practical issues. Therefore, the findings of the pretest need to be appraised with care. Finally, there may be potential confounding variables that were not measured in the study but could potentially impact sleep outcomes. The potential influence of these variables on the observed association should be considered when interpreting the study findings.

Conclusions and recommendations

The findings of this cross-sectional study reveal a concerning trend of poor sleep quality among adolescents in Brunei, with more than half of the sampled adolescents showing evidence of suboptimal sleep. Specifically, the sleep disturbance component was found to be the most significantly impacted domain of sleep quality. Adolescents spend an average of 12 h using digital screens on weekends, and most of this time is spent on smartphones. Additionally, statistically significant associations were established between the female gender, older adolescents and screen use on weekends with poor sleep quality.

These findings underscore the need for targeted interventions to address the harmful impacts of excessive screen time on sleep quality, as this behaviour may be a risk factor contributing to the high rates of poor sleep quality observed among adolescents in Brunei. Given the lack of extensive research on this topic in Brunei, it is recommended that educational programs be developed to raise awareness of the risks associated with poor-quality sleep among adolescents. These programs should focus on promoting good sleep hygiene practices, including the importance of establishing regular sleep-wake cycles. Furthermore, guidelines and recommendations should be provided to parents and students, especially female students regarding the adverse consequences of excessive screen time, particularly with smartphones. A subsequent evaluation targeting female students in tertiary institutions is advisable to ascertain the extent to which sleep and screen time patterns undergo modifications during the transition to university life. Additionally, future research ventures should focus on conducting longitudinal or experimental studies to identify other exposures related to sleep quality and other health-related consequences of excessive screen time. This would allow for the exploration of the temporal relationship between screen time and sleep quality, as well as the examination of other factors that may contribute to poor sleep quality among adolescents in Brunei.

In conclusion, the results of this study highlight the crucial need for interventions to address the high rates of poor sleep quality among adolescents in Brunei, particularly concerning excessive screen time. Therefore, it is vital that healthy sleep practices are endorsed and awareness is raised regarding the undesirable consequences of excessive screen time.

Abbreviations

CI

Confidence interval

PSQI

Pittsburgh sleep quality index

STQ

Screen time questionnaire

SD

Standard deviation

SLR

Simple Linear Regression

MLR

Multiple Linear regression

UAE

United Arab Emirates

Author contributions

LN contributed to the conception of the work, designing the study, supervising the data collection, data analysis, reviewing and finalizing the paper. SH contributed to designing the study, managing the data collection, data analysis, and drafting, reviewing and finalizing the paper. SS contributed to reviewing the methodology, reviewing and finalizing the paper. ZRM contributed to designing the study, assisting with the data collection, reviewing and finalizing the paper. NT contributed to designing the study, assisting with the data collection, reviewing and finalizing the paper. ZMT contributed to designing the study, assisting with the data collection, reviewing and finalizing the paper.

Funding

The research was self-funded.

Data availability

The datasets used and/or analysed during the current study are not available due to ethical reasons.

Declarations

Ethics approval and consent to participate

Before the commencement of the study, ethical approval was obtained from the Pengiran Anak Puteri Rashidah Sa’adatul Bolkiah Institute of Health Sciences Ethics Committee (PAPRSBIHSEC) in November 2022 (UBD/PAPRSBIHSREC/2022/141) at the Universiti Brunei Darussalam. Permission to conduct the study was taken from the Director of the Department of School in the Ministry of Education (MOE), following which the respective school principals were then approached, and further authorisation to conduct the study was sought. The permission to use the questionnaire was also obtained from the original authors of the questionnaire before the study. Participant information sheets for both parents and students were prepared and given together with a consent form for both parents and students. Informed and written consent was obtained from all subjects and their legal guardians during data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.